Publications by authors named "Benjamin M Neale"

208 Publications

Human genetic analyses of organelles highlight the nucleus in age-related trait heritability.

Elife 2021 09 1;10. Epub 2021 Sep 1.

Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, United States.

Most age-related human diseases are accompanied by a decline in cellular organelle integrity, including impaired lysosomal proteostasis and defective mitochondrial oxidative phosphorylation. An open question, however, is the degree to which inherited variation in or near genes encoding each organelle contributes to age-related disease pathogenesis. Here, we evaluate if genetic loci encoding organelle proteomes confer greater-than-expected age-related disease risk. As mitochondrial dysfunction is a 'hallmark' of aging, we begin by assessing nuclear and mitochondrial DNA loci near genes encoding the mitochondrial proteome and surprisingly observe a lack of enrichment across 24 age-related traits. Within nine other organelles, we find no enrichment with one exception: the nucleus, where enrichment emanates from nuclear transcription factors. In agreement, we find that genes encoding several organelles tend to be 'haplosufficient,' while we observe strong purifying selection against heterozygous protein-truncating variants impacting the nucleus. Our work identifies common variation near transcription factors as having outsize influence on age-related trait risk, motivating future efforts to determine if and how this inherited variation then contributes to observed age-related organelle deterioration.
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http://dx.doi.org/10.7554/eLife.68610DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8476128PMC
September 2021

Problems with Using Polygenic Scores to Select Embryos.

N Engl J Med 2021 07;385(1):78-86

From the University of Southern California (P.T.) and the University of California, Los Angeles (D.J.B.) - both in Los Angeles; Geisinger Health System, Danville, PA (M.N.M.); the National Bureau of Economic Research (N.W., D.C., D.J.B., D.L.), Harvard University (E.H., S.H., D.L.), and the Broad Institute of Harvard and MIT (A.R.M., B.M.N., H.L.R., S.H.) - all in Cambridge, MA; Massachusetts General Hospital (A.R.M., B.M.N., H.L.R.), Harvard Medical School (A.R.M., B.M.N., H.L.R., L.W.-H.), and Brigham and Women's Hospital (L.W.-H.) - all in Boston; New York University, New York (D.C.); and the University of Queensland, Brisbane, Australia (P.M.V.).

Companies have recently begun to sell a new service to patients considering in vitro fertilization: embryo selection based on polygenic scores (ESPS). These scores represent individualized predictions of health and other outcomes derived from genomewide association studies in adults to partially predict these outcomes. This article includes a discussion of many factors that lower the predictive power of polygenic scores in the context of embryo selection and quantifies these effects for a variety of clinical and nonclinical traits. Also discussed are potential unintended consequences of ESPS (including selecting for adverse traits, altering population demographics, exacerbating inequalities in society, and devaluing certain traits). Recommendations for the responsible communication about ESPS by practitioners are provided, and a call for a society-wide conversation about this technology is made. (Funded by the National Institute on Aging and others.).
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http://dx.doi.org/10.1056/NEJMsr2105065DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387884PMC
July 2021

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.

Nat Genet 2021 06 17;53(6):817-829. Epub 2021 May 17.

Department of Neuroscience, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.
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http://dx.doi.org/10.1038/s41588-021-00857-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192451PMC
June 2021

Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations.

Hum Mol Genet 2021 07;30(16):1521-1534

Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

It is important to study the genetics of complex traits in diverse populations. Here, we introduce covariate-adjusted linkage disequilibrium (LD) score regression (cov-LDSC), a method to estimate SNP-heritability (${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}})$ and its enrichment in homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the genome-wide association studies samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ by 10-60% in admixed populations; in contrast, cov-LDSC is robustly accurate. We apply cov-LDSC to genotyping data from 8124 individuals, mostly of admixed ancestry, from the Slim Initiative in Genomic Medicine for the Americas study, and to approximately 161 000 Latino-ancestry individuals, 47 000 African American-ancestry individuals and 135 000 European-ancestry individuals, as classified by 23andMe. We estimate ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and detect heritability enrichment in three quantitative and five dichotomous phenotypes, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals to date. Most traits have high concordance of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$. We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size $ \tau $* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in the Latino-, African American- and European-ancestry populations, respectively. Our approach is a powerful way to analyze genetic data for complex traits from admixed populations.
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http://dx.doi.org/10.1093/hmg/ddab130DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330913PMC
July 2021

Genetic analyses identify widespread sex-differential participation bias.

Nat Genet 2021 05 22;53(5):663-671. Epub 2021 Apr 22.

Faculty of Behavioural and Movement Sciences, Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands.

Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of participation bias is challenging since it requires the genotypes of unseen individuals. Here we demonstrate that it is possible to estimate comparative biases by performing a genome-wide association study contrasting one subgroup versus another. For example, we showed that sex exhibits artifactual autosomal heritability in the presence of sex-differential participation bias. By performing a genome-wide association study of sex in approximately 3.3 million males and females, we identified over 158 autosomal loci spuriously associated with sex and highlighted complex traits underpinning differences in study participation between the sexes. For example, the body mass index-increasing allele at FTO was observed at higher frequency in males compared to females (odds ratio = 1.02, P = 4.4 × 10). Finally, we demonstrated how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.
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http://dx.doi.org/10.1038/s41588-021-00846-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611642PMC
May 2021

Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations.

Am J Hum Genet 2021 04 25;108(4):656-668. Epub 2021 Mar 25.

Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.

Genetic studies in underrepresented populations identify disproportionate numbers of novel associations. However, most genetic studies use genotyping arrays and sequenced reference panels that best capture variation most common in European ancestry populations. To compare data generation strategies best suited for underrepresented populations, we sequenced the whole genomes of 91 individuals to high coverage as part of the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study with participants from Ethiopia, Kenya, South Africa, and Uganda. We used a downsampling approach to evaluate the quality of two cost-effective data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole-genome sequencing data. We show that low-coverage sequencing at a depth of ≥4× captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1×) performed comparably to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation; 4× sequencing detects 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, effectively identify novel variation particularly in underrepresented populations, and present opportunities to enhance variant discovery at a cost similar to traditional approaches.
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http://dx.doi.org/10.1016/j.ajhg.2021.03.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059370PMC
April 2021

Response to Comment on "Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior".

Science 2021 03;371(6536)

Centre for Psychology and Evolution, School of Psychology, University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia.

Hamer argue that the variable "ever versus never had a same-sex partner" does not capture the complexity of human sexuality. We agree and said so in our paper. But Hamer neglect to mention that we also reported follow-up analyses showing substantial overlap of the genetic influences on our main variable and on more nuanced measures of sexual behavior, attraction, and identity.
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http://dx.doi.org/10.1126/science.aba5693DOI Listing
March 2021

Examining Sex-Differentiated Genetic Effects Across Neuropsychiatric and Behavioral Traits.

Biol Psychiatry 2021 06 9;89(12):1127-1137. Epub 2021 Jan 9.

Psychiatric & Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.

Background: The origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations, we tested for a sex-differentiated genetic architecture within and between traits.

Methods: Using European ancestry genome-wide association summary statistics for 20 neuropsychiatric and behavioral traits, we tested for sex differences in single nucleotide polymorphism (SNP)-based heritability and genetic correlation (r < 1). For each trait, we computed per-SNP z scores from sex-stratified regression coefficients and identified genes with sex-differentiated effects using a gene-based approach. We calculated correlation coefficients between z scores to test for shared sex-differentiated effects. Finally, we tested for sex differences in across-trait genetic correlations.

Results: We observed no consistent sex differences in SNP-based heritability. Between-sex, within-trait genetic correlations were high, although <1 for educational attainment and risk-taking behavior. We identified 4 genes with significant sex-differentiated effects across 3 traits. Several trait pairs shared sex-differentiated effects. The top genes with sex-differentiated effects were enriched for multiple gene sets, including neuron- and synapse-related sets. Most between-trait genetic correlation estimates were not significantly different between sexes, with exceptions (educational attainment and risk-taking behavior).

Conclusions: Sex differences in the common autosomal genetic architecture of neuropsychiatric and behavioral phenotypes are small and polygenic and unlikely to fully account for observed sex-differentiated attributes. Larger sample sizes are needed to identify sex-differentiated effects for most traits. For well-powered studies, we identified genes with sex-differentiated effects that were enriched for neuron-related and other biological functions. This work motivates further investigation of genetic and environmental influences on sex differences.
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http://dx.doi.org/10.1016/j.biopsych.2020.12.024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163257PMC
June 2021

Publisher Correction: Clinical use of current polygenic risk scores may exacerbate health disparities.

Nat Genet 2021 May;53(5):763

Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.

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http://dx.doi.org/10.1038/s41588-021-00797-zDOI Listing
May 2021

Risk variants and polygenic architecture of disruptive behavior disorders in the context of attention-deficit/hyperactivity disorder.

Nat Commun 2021 01 25;12(1):576. Epub 2021 Jan 25.

The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood psychiatric disorder often comorbid with disruptive behavior disorders (DBDs). Here, we report a GWAS meta-analysis of ADHD comorbid with DBDs (ADHD + DBDs) including 3802 cases and 31,305 controls. We identify three genome-wide significant loci on chromosomes 1, 7, and 11. A meta-analysis including a Chinese cohort supports that the locus on chromosome 11 is a strong risk locus for ADHD + DBDs across European and Chinese ancestries (rs7118422, P = 3.15×10, OR = 1.17). We find a higher SNP heritability for ADHD + DBDs (h = 0.34) when compared to ADHD without DBDs (h = 0.20), high genetic correlations between ADHD + DBDs and aggressive (r = 0.81) and anti-social behaviors (r = 0.82), and an increased burden (polygenic score) of variants associated with ADHD and aggression in ADHD + DBDs compared to ADHD without DBDs. Our results suggest an increased load of common risk variants in ADHD + DBDs compared to ADHD without DBDs, which in part can be explained by variants associated with aggressive behavior.
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http://dx.doi.org/10.1038/s41467-020-20443-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7835232PMC
January 2021

Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power.

Nat Genet 2021 02 18;53(2):195-204. Epub 2021 Jan 18.

Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.

Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants.
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http://dx.doi.org/10.1038/s41588-020-00766-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867648PMC
February 2021

Synaptic processes and immune-related pathways implicated in Tourette syndrome.

Transl Psychiatry 2021 01 18;11(1):56. Epub 2021 Jan 18.

Sorbonne Universités, UPMC Université Paris 06, UMR S 1127, CNRS UMR 7225, ICM, Paris, France.

Tourette syndrome (TS) is a neuropsychiatric disorder of complex genetic architecture involving multiple interacting genes. Here, we sought to elucidate the pathways that underlie the neurobiology of the disorder through genome-wide analysis. We analyzed genome-wide genotypic data of 3581 individuals with TS and 7682 ancestry-matched controls and investigated associations of TS with sets of genes that are expressed in particular cell types and operate in specific neuronal and glial functions. We employed a self-contained, set-based association method (SBA) as well as a competitive gene set method (MAGMA) using individual-level genotype data to perform a comprehensive investigation of the biological background of TS. Our SBA analysis identified three significant gene sets after Bonferroni correction, implicating ligand-gated ion channel signaling, lymphocytic, and cell adhesion and transsynaptic signaling processes. MAGMA analysis further supported the involvement of the cell adhesion and trans-synaptic signaling gene set. The lymphocytic gene set was driven by variants in FLT3, raising an intriguing hypothesis for the involvement of a neuroinflammatory element in TS pathogenesis. The indications of involvement of ligand-gated ion channel signaling reinforce the role of GABA in TS, while the association of cell adhesion and trans-synaptic signaling gene set provides additional support for the role of adhesion molecules in neuropsychiatric disorders. This study reinforces previous findings but also provides new insights into the neurobiology of TS.
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http://dx.doi.org/10.1038/s41398-020-01082-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814139PMC
January 2021

The genetic architecture of sporadic and multiple consecutive miscarriage.

Nat Commun 2020 11 25;11(1):5980. Epub 2020 Nov 25.

University of Queensland, St Lucia, QLD, Australia.

Miscarriage is a common, complex trait affecting ~15% of clinically confirmed pregnancies. Here we present the results of large-scale genetic association analyses with 69,054 cases from five different ancestries for sporadic miscarriage, 750 cases of European ancestry for multiple (≥3) consecutive miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association (rs146350366, minor allele frequency (MAF) 1.2%, P = 3.2 × 10, odds ratio (OR) = 1.4) for sporadic miscarriage in our European ancestry meta-analysis and three genome-wide significant associations for multiple consecutive miscarriage (rs7859844, MAF = 6.4%, P = 1.3 × 10, OR = 1.7; rs143445068, MAF = 0.8%, P = 5.2 × 10, OR = 3.4; rs183453668, MAF = 0.5%, P = 2.8 × 10, OR = 3.8). We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability, and genetic correlation analyses. Our results show that miscarriage etiopathogenesis is partly driven by genetic variation potentially related to placental biology, and illustrate the utility of large-scale biobank data for understanding this pregnancy complication.
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http://dx.doi.org/10.1038/s41467-020-19742-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689465PMC
November 2020

Inherited myeloproliferative neoplasm risk affects haematopoietic stem cells.

Nature 2020 10 14;586(7831):769-775. Epub 2020 Oct 14.

Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Myeloproliferative neoplasms (MPNs) are blood cancers that are characterized by the excessive production of mature myeloid cells and arise from the acquisition of somatic driver mutations in haematopoietic stem cells (HSCs). Epidemiological studies indicate a substantial heritable component of MPNs that is among the highest known for cancers. However, only a limited number of genetic risk loci have been identified, and the underlying biological mechanisms that lead to the acquisition of MPNs remain unclear. Here, by conducting a large-scale genome-wide association study (3,797 cases and 1,152,977 controls), we identify 17 MPN risk loci (P < 5.0 × 10), 7 of which have not been previously reported. We find that there is a shared genetic architecture between MPN risk and several haematopoietic traits from distinct lineages; that there is an enrichment for MPN risk variants within accessible chromatin of HSCs; and that increased MPN risk is associated with longer telomere length in leukocytes and other clonal haematopoietic states-collectively suggesting that MPN risk is associated with the function and self-renewal of HSCs. We use gene mapping to identify modulators of HSC biology linked to MPN risk, and show through targeted variant-to-function assays that CHEK2 and GFI1B have roles in altering the function of HSCs to confer disease risk. Overall, our results reveal a previously unappreciated mechanism for inherited MPN risk through the modulation of HSC function.
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http://dx.doi.org/10.1038/s41586-020-2786-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606745PMC
October 2020

Genome-wide association study identifies 48 common genetic variants associated with handedness.

Nat Hum Behav 2021 01 28;5(1):59-70. Epub 2020 Sep 28.

Institute of Biological Psychiatry, Mental Health Services of Copenhagen, Copenhagen, Denmark.

Handedness has been extensively studied because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and the International Handedness Consortium, we conducted a genome-wide association meta-analysis of handedness (N = 1,766,671). We found 41 loci associated (P < 5 × 10) with left-handedness and 7 associated with ambidexterity. Tissue-enrichment analysis implicated the CNS in the aetiology of handedness. Pathways including regulation of microtubules and brain morphology were also highlighted. We found suggestive positive genetic correlations between left-handedness and neuropsychiatric traits, including schizophrenia and bipolar disorder. Furthermore, the genetic correlation between left-handedness and ambidexterity is low (r = 0.26), which implies that these traits are largely influenced by different genetic mechanisms. Our findings suggest that handedness is highly polygenic and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders.
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http://dx.doi.org/10.1038/s41562-020-00956-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7116623PMC
January 2021

A data-driven medication score predicts 10-year mortality among aging adults.

Sci Rep 2020 09 25;10(1):15760. Epub 2020 Sep 25.

Institute for Molecular Medicine Finland FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.

Health differences among the elderly and the role of medical treatments are topical issues in aging societies. We demonstrate the use of modern statistical learning methods to develop a data-driven health measure based on 21 years of pharmacy purchase and mortality data of 12,047 aging individuals. The resulting score was validated with 33,616 individuals from two fully independent datasets and it is strongly associated with all-cause mortality (HR 1.18 per point increase in score; 95% CI 1.14-1.22; p = 2.25e-16). When combined with Charlson comorbidity index, individuals with elevated medication score and comorbidity index had over six times higher risk (HR 6.30; 95% CI 3.84-10.3; AUC = 0.802) compared to individuals with a protective score profile. Alone, the medication score performs similarly to the Charlson comorbidity index and is associated with polygenic risk for coronary heart disease and type 2 diabetes.
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http://dx.doi.org/10.1038/s41598-020-72045-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519677PMC
September 2020

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.

Nat Genet 2020 09 24;52(9):969-983. Epub 2020 Aug 24.

Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.
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http://dx.doi.org/10.1038/s41588-020-0676-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483769PMC
September 2020

Statistical Power and the Classical Twin Design.

Twin Res Hum Genet 2020 04;23(2):87-89

Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.

Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.
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http://dx.doi.org/10.1017/thg.2020.46DOI Listing
April 2020

Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics.

Sci Transl Med 2020 06;12(549)

Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK.

Inhibition of sclerostin is a therapeutic approach to lowering fracture risk in patients with osteoporosis. However, data from phase 3 randomized controlled trials (RCTs) of romosozumab, a first-in-class monoclonal antibody that inhibits sclerostin, suggest an imbalance of serious cardiovascular events, and regulatory agencies have issued marketing authorizations with warnings of cardiovascular disease. Here, we meta-analyze published and unpublished cardiovascular outcome trial data of romosozumab and investigate whether genetic variants that mimic therapeutic inhibition of sclerostin are associated with higher risk of cardiovascular disease. Meta-analysis of up to three RCTs indicated a probable higher risk of cardiovascular events with romosozumab. Scaled to the equivalent dose of romosozumab (210 milligrams per month; 0.09 grams per square centimeter of higher bone mineral density), the genetic variants were associated with lower risk of fracture and osteoporosis (commensurate with the therapeutic effect of romosozumab) and with a higher risk of myocardial infarction and/or coronary revascularization and major adverse cardiovascular events. The same variants were also associated with increased risk of type 2 diabetes mellitus and higher systolic blood pressure and central adiposity. Together, our findings indicate that inhibition of sclerostin may elevate cardiovascular risk, warranting a rigorous evaluation of the cardiovascular safety of romosozumab and other sclerostin inhibitors.
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http://dx.doi.org/10.1126/scitranslmed.aay6570DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7116615PMC
June 2020

Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.

Am J Hum Genet 2020 07 28;107(1):46-59. Epub 2020 May 28.

Division of Genetics, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Altius Institute for Biomedical Sciences, Seattle, WA 98121, USA. Electronic address:

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait's heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.
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http://dx.doi.org/10.1016/j.ajhg.2020.05.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7332650PMC
July 2020

The mutational constraint spectrum quantified from variation in 141,456 humans.

Nature 2020 05 27;581(7809):434-443. Epub 2020 May 27.

Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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http://dx.doi.org/10.1038/s41586-020-2308-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334197PMC
May 2020

A structural variation reference for medical and population genetics.

Nature 2020 05 27;581(7809):444-451. Epub 2020 May 27.

Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Structural variants (SVs) rearrange large segments of DNA and can have profound consequences in evolution and human disease. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD) have become integral in the interpretation of single-nucleotide variants (SNVs). However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25-29% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9% of samples, and estimate that 0.13% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings. This SV resource is freely distributed via the gnomAD browser and will have broad utility in population genetics, disease-association studies, and diagnostic screening.
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http://dx.doi.org/10.1038/s41586-020-2287-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334194PMC
May 2020

Mapping and characterization of structural variation in 17,795 human genomes.

Nature 2020 07 27;583(7814):83-89. Epub 2020 May 27.

McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA.

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.
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http://dx.doi.org/10.1038/s41586-020-2371-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7547914PMC
July 2020

Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts.

Nat Genet 2020 06 18;52(6):634-639. Epub 2020 May 18.

Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.

With very large sample sizes, biobanks provide an exciting opportunity to identify genetic components of complex traits. To analyze rare variants, region-based multiple-variant aggregate tests are commonly used to increase power for association tests. However, because of the substantial computational cost, existing region-based tests cannot analyze hundreds of thousands of samples while accounting for confounders such as population stratification and sample relatedness. Here we propose a scalable generalized mixed-model region-based association test, SAIGE-GENE, that is applicable to exome-wide and genome-wide region-based analysis for hundreds of thousands of samples and can account for unbalanced case-control ratios for binary traits. Through extensive simulation studies and analysis of the HUNT study with 69,716 Norwegian samples and the UK Biobank data with 408,910 White British samples, we show that SAIGE-GENE can efficiently analyze large-sample data (N > 400,000) with type I error rates well controlled.
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http://dx.doi.org/10.1038/s41588-020-0621-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7871731PMC
June 2020
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